Data-Driven One-Dimensional Modeling of Pseudoshocks

Abstract

In dual-mode scramjet engines, the isolator comprises complex shock structures that provide the necessary compression of incoming air to sustain combustion farther downstream. This region of pressure increase is termed a pseudoshock. For stable scramjet design, it is essential to ensure sufficient length of isolator to fit the pseudoshock and achieve the target compression. Hence, models that can predict the pressure rise across and the length of the pseudoshock are valuable in this context. Although several empirical and reduced-order models are available in the literature, it is found that such models do not reliably predict the pseudoshock characteristics. To address this issue, a one-dimensional model is developed from a flux-conserved approach, which captures the pressure growth profiles for a wide range of Mach numbers. The model parameters are initially calibrated using a genetic algorithm but were found to exhibit variations based on experimental conditions. Using these results as prior information, a Bayesian approach is used to describe the one-dimensional model and related uncertainties statistically. This approach provided interesting insights, notably the ability to reduce the number of parameters. The resulting model accurately reproduces the pseudoshock characteristics over the range of operating conditions tested.